4.5 Article

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2023JF007219

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salt marshes; water levels; vegetation; UAVSAR; AVIRIS-NG

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Water movement in coastal wetlands is influenced by topography, vegetation characteristics, and hydrological processes. This study utilizes remote sensing data to analyze water-level changes and vegetation contributions in marshes, revealing the importance of small geomorphic features and the minor role of vegetation in water retention.
Water movement in coastal wetlands is affected by spatial differences in topography and vegetation characteristics as well as by complex hydrological processes operating at different time scales. Traditionally, numerical models have been used to explore the hydrodynamics of these valuable ecosystems. However, we still do not know how well such models simulate water-level fluctuations beneath the vegetation canopy since we lack extensive field data to test the model results against observations. This study utilizes remotely sensed images of sub-canopy water-level change to understand how marshes drain water during falling tides. We employ rapid repeat interferometric observations from the NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument to analyze the spatial variability in water-level change within a complex of marshes in Terrebonne Bay, Louisiana. We also used maps of herbaceous aboveground biomass derived from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation to evaluate vegetation contribution to such variability. This study reveals that the distribution of water-level change under salt marsh canopies is strongly influenced by the presence of small geomorphic features (<10 m) in the marsh landscape (i.e., levees, tidal channels), whereas vegetation plays a minor role in retaining water on the platform. This new type of high-resolution remote sensing data offers the opportunity to study the feedback between hydrodynamics, topography and biology throughout wetlands at an unprecedented spatial resolution and test the capability of numerical models to reproduce such patterns. Our results are essential for predicting the vulnerability of these delicate environments to climate change.

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